---
title: "Consumer Complaint Data"
author: "Diana Rodriguez"
date: "5/8/2022"
output:
flexdashboard::flex_dashboard:
orientation: rows
social: menu
source_code: embed
---
```{r setup, include=FALSE}
library(tidyverse)
library(lubridate)
library(janitor)
library(readxl)
library(writexl)
library(viridis)
library(dplyr)
library(stringr)
library(plotly)
library(tigris)
library(sf)
library(tmap)
library(tmaptools)
library(htmltools)
library(janitor)
library(rmapshaper)
library(here)
library(flexdashboard)
library(DT)
options(tigris_class = "sf")
```
```{r include=FALSE}
complaints_raw <- read_rds("data/complaints.rds")
complaints <- complaints_raw %>%
clean_names()
```
```{r include=FALSE}
complaints %>%
filter(state == "CA") %>%
count(issue,sort = TRUE) %>%
arrange(desc(issue))
```
##
# Consumer Complaint Data California 2020
## Top Company Complaints in California
```{r, fig.width = 10, fig.height = 3}
CA <- complaints %>%
filter(state == "CA")
CA_companies <- CA %>%
group_by(company, issue) %>%
count(issue, sort = TRUE)
Top_CA_companies <- head(CA_companies,5)
Top_co <-ggplot(Top_CA_companies, aes(x = reorder(company,issue), y = n))+
geom_col(color = "#FD8F6B", fill = "#FD8F6B")+
theme(axis.text.x.left = element_text(size = .05))+
coord_flip()+
scale_y_continuous(name = "amount of complaints", labels = scales::comma) +
scale_x_discrete(name = "companies") +
labs(title = "California Company Complaints",
subtitle = "2020") +
theme_minimal()
ggplotly(Top_co)
```
##
## Most Common Categories of Complaints
```{r, fig.width = 10, fig.height = 3}
CA_complaints <- CA %>%
group_by(issue) %>%
count(issue, sort = TRUE)
Top_complaints <-
head(CA_complaints,5)
top_is <- ggplot(Top_complaints, aes(x = reorder(issue,n), y = n))+
geom_col(color = "#46A5E5", fill = "#46A5E5")+
theme(axis.text.x.left = element_text(size = 2))+
coord_flip()+
scale_y_continuous(name = "Number of Complaints ", labels = scales::comma) +
scale_x_discrete(name = "Kinds of Complaints") +
labs(title = "California Common Complaints",
subtitle = "2020") +
theme_minimal()
ggplotly(top_is)
```
row
-------------------------------------------------------------------------------
## Analyzing Debt Collection Complaints x Companies
```{r, fig.width = 10, fig.height = 3}
CA_debt <- CA %>%
filter(product == "Debt collection") %>%
group_by(company, issue) %>%
summarise(issue) %>%
count(issue, sort = TRUE)
Top_CA_debt_complaints <-
head(CA_debt,10)
Top_db <-ggplot(Top_CA_debt_complaints, aes(x = reorder(company,n), y = n))+
geom_col(color = "#F7B7B7", fill = "#F7B7B7")+
theme(axis.text.x.left = element_text(size = 5))+
coord_flip()+
scale_y_continuous(name = "Number of Complaints", labels = scales::comma) +
scale_x_discrete(name = "Companies") +
labs(title = "Companies x Debt Collection Related Complaints",
subtitle = "2020") +
theme_minimal()
ggplotly(Top_db)
```